Why is gradient ascent necessary when training Actor Critic agents?
It might seem to give the same update direction but would it converge to desirable policy parameters?

Actor-Critic is proposed alongside the policy gradient theorem in Sutton 1999. It is shown to maximize the state-value function. If you are able to show that the technique of yours is, in fact, maximizing some desirable objective function, you could propose it with some soundness as well.